Instructions to use Sumit0987/naradaba-sqlcoder-alfa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sumit0987/naradaba-sqlcoder-alfa with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Sumit0987/naradaba-sqlcoder-alfa", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use Sumit0987/naradaba-sqlcoder-alfa with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Sumit0987/naradaba-sqlcoder-alfa to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Sumit0987/naradaba-sqlcoder-alfa to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Sumit0987/naradaba-sqlcoder-alfa to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Sumit0987/naradaba-sqlcoder-alfa", max_seq_length=2048, )
Update adapter_config.json
Browse filesadded "model_type": "llama",
- adapter_config.json +1 -0
adapter_config.json
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"up_proj"
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],
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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"use_dora": false,
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"use_rslora": false
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"up_proj"
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],
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"task_type": "CAUSAL_LM",
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"model_type": "llama",
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"trainable_token_indices": null,
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"use_dora": false,
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"use_rslora": false
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